

'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_rembert import RemBertTokenizer
else:
				A_	= None

A_	= logging.get_logger(__name__)
A_	= {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

A_	= {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

A_	= {
    "google/rembert": 256,
}

A_	= "▁"


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_           =					PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_           =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_           =					RemBertTokenizer
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_="[CLS]"   ,			SCREAMING_SNAKE_CASE_="[SEP]"   ,			SCREAMING_SNAKE_CASE_="<unk>"   ,			SCREAMING_SNAKE_CASE_="[SEP]"   ,			SCREAMING_SNAKE_CASE_="<pad>"   ,			SCREAMING_SNAKE_CASE_="[CLS]"   ,			SCREAMING_SNAKE_CASE_="[MASK]"   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      AddedToken(SCREAMING_SNAKE_CASE_   ,			lstrip=SCREAMING_SNAKE_CASE_   ,			rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		) else mask_token

							super().__init__(
							    SCREAMING_SNAKE_CASE_   ,			tokenizer_file=SCREAMING_SNAKE_CASE_   ,			do_lower_case=SCREAMING_SNAKE_CASE_   ,			remove_space=SCREAMING_SNAKE_CASE_   ,			keep_accents=SCREAMING_SNAKE_CASE_   ,			bos_token=SCREAMING_SNAKE_CASE_   ,			eos_token=SCREAMING_SNAKE_CASE_   ,			unk_token=SCREAMING_SNAKE_CASE_   ,			sep_token=SCREAMING_SNAKE_CASE_   ,			pad_token=SCREAMING_SNAKE_CASE_   ,			cls_token=SCREAMING_SNAKE_CASE_   ,			mask_token=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_   ,			)

							lowerCamelCase_  				=      do_lower_case
							lowerCamelCase_  				=      remove_space
							lowerCamelCase_  				=      keep_accents
							lowerCamelCase_  				=      vocab_file
							lowerCamelCase_  				=      False if not self.vocab_file else True
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     List[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      [self.sep_token_id]
							lowerCamelCase_  				=      [self.cls_token_id]
							if token_ids_a is None:
												return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = False		)  ->     List[int]:






							'''simple docstring'''



							if already_has_special_tokens:
												if token_ids_a is not None:
																	raise ValueError(
																	    'You should not supply a second sequence if the provided sequence of '
																	    'ids is already formatted with special tokens for the model.'		)
												return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
												return [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1]
							return [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1]
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     List[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      [self.sep_token_id]
							lowerCamelCase_  				=      [self.cls_token_id]

							if token_ids_a is None:
												return len(cls + token_ids_a + sep		) * [0]
							return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     Tuple[str]:






							'''simple docstring'''



							if not os.path.isdir(SCREAMING_SNAKE_CASE_		):
												logger.error('Vocabulary path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE_		)		)
												return
							lowerCamelCase_  				=      os.path.join(
							    SCREAMING_SNAKE_CASE_   ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(SCREAMING_SNAKE_CASE_		):
												copyfile(self.vocab_file   ,			SCREAMING_SNAKE_CASE_		)

							return (out_vocab_file,)









'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



